We are going to analyze an invasive ductal carcinoma breast tissue section from 10x Genomicsis Visium Gene Expression platform.
For the analysis we will mainly use the R package Seurat.
You can download the data from this website or using curl, as shown below. We do not need to do it because it is already in the data folder.
# download files
curl -O https://cf.10xgenomics.com/samples/spatial-exp/1.1.0/V1_Breast_Cancer_Block_A_Section_1/V1_Breast_Cancer_Block_A_Section_1_filtered_feature_bc_matrix.h5
curl -O https://cf.10xgenomics.com/samples/spatial-exp/1.1.0/V1_Breast_Cancer_Block_A_Section_1/V1_Breast_Cancer_Block_A_Section_1_spatial.tar.gzThese are the libraries we are going to use:
library(tidyverse)
library(Seurat)
library(patchwork)# set seed for reproducibility purposes
set.seed(1243)
# create palette for the cell types from the pals package
cell_type_palette <- c(
"#5A5156", "#E4E1E3", "#F6222E", "#FE00FA",
"#16FF32", "#3283FE", "#FEAF16", "#B00068",
"#1CFFCE", "#90AD1C", "#2ED9FF", "#DEA0FD",
"#AA0DFE", "#F8A19F", "#325A9B", "#C4451C",
"#1C8356", "#85660D", "#B10DA1", "#FBE426",
"#1CBE4F", "#FA0087", "#FC1CBF", "#F7E1A0",
"#C075A6", "#782AB6", "#AAF400", "#BDCDFF",
"#822E1C", "#B5EFB5", "#7ED7D1", "#1C7F93",
"#D85FF7", "#683B79", "#66B0FF", "#3B00FB")We start by loading the data Visium data. Note that here we start by already loading the filtered expression data. This means that we are only keeping those spots that overlap with the tissue as determined by spaceranger.
If we expect there to be overpermabilization of the tissue or want to get a more general look we can load the raw HDF5 file instead. The raw file contains the matrix for all 5.000 spots comprising the capture area.
sp_obj <- Load10X_Spatial(
data.dir = "data", # Directory where these data is stored
filename = "filtered_feature_bc_matrix.h5", # Name of H5 file containing the feature barcode matrix
assay = "Spatial", # Name of assay
slice = "slice1", # Name of the image
filter.matrix = TRUE # Only keep spots that have been determined to be over tissue
)The visium data from 10x consists is stored in a Seurat object. This object has a very similar structure to the scRNAseq object:
sp_obj[["Spatial"]][1:5, 1:5]## 5 x 5 sparse Matrix of class "dgCMatrix"
## AAACAAGTATCTCCCA-1 AAACACCAATAACTGC-1 AAACAGAGCGACTCCT-1
## MIR1302-2HG . . .
## FAM138A . . .
## OR4F5 . . .
## AL627309.1 . . .
## AL627309.3 . . .
## AAACAGGGTCTATATT-1 AAACAGTGTTCCTGGG-1
## MIR1302-2HG . .
## FAM138A . .
## OR4F5 . .
## AL627309.1 . .
## AL627309.3 . .It adds one slot which contains the images of the Visium experiments as seen below:
sp_obj@images## $slice1
## Spatial data from the VisiumV1 technology for 3798 samples
## Associated assay: Spatial
## Image key: slice1_We can visualize the image as follows (we will also store this for later):
(img <- SpatialPlot(
sp_obj, # Name of the Seurat Object
pt.size = 0, # Point size to see spots on the tissue
crop = FALSE # Wether to crop to see only tissue section
) +
NoLegend()
)
sp_obj@images$slice1@scale.factors## $spot
## [1] 0.08250825
##
## $fiducial
## [1] 286.7012
##
## $hires
## [1] 0.08250825
##
## $lowres
## [1] 0.02475247
##
## attr(,"class")
## [1] "scalefactors"The goal of this step is to remove poor quality spots and lowly captured genes. To do so we will go over some basic QC steps. Furthermore, due to the nature of the assay during library preparation there can be some lateral diffusion of transcripts. If there are spots not overlapping with the tissue we also need to remove them since these are artifacts of the experiment.
As with single-cell objects, we have some important features that we can use to filter out bad quality spots.
umi_vln_plt <- VlnPlot(
sp_obj,
features = "nCount_Spatial",
pt.size = 0.1) +
NoLegend()
umi_sp_plt <- SpatialFeaturePlot(
sp_obj,
features = "nCount_Spatial")
umi_vln_plt | umi_sp_plt | img
The variability in the distribution of UMIs is related to the tissue architecture, i.e. tumoral regions have a higher cell density than fibrotic regions and thus overlapping spots contain higher counts.
feat_vln_plt <- VlnPlot(
sp_obj,
features = "nFeature_Spatial",
pt.size = 0.1) +
NoLegend()
feat_sp_plt <- SpatialFeaturePlot(
sp_obj,
features = "nFeature_Spatial")
feat_vln_plt | feat_sp_plt | img
Again, here we can see how the number of genes per spot correlates with the structure of the tissue.
We have to compute this two values by calculating the percentage of reads per spot that belong to mitochondrial/ribosomal genes.
# Mitochondrial content
sp_obj[["mt.content"]] <- PercentageFeatureSet(
object = sp_obj,
pattern = "^MT-")
summary(sp_obj[["mt.content"]])## mt.content
## Min. : 1.118
## 1st Qu.: 2.761
## Median : 3.504
## Mean : 4.009
## 3rd Qu.: 4.678
## Max. :14.415# Ral contentibosomal
sp_obj[["rb.content"]] <- PercentageFeatureSet(
object = sp_obj,
pattern = "^RPL|^RPS")
summary(sp_obj[["rb.content"]])## rb.content
## Min. : 6.697
## 1st Qu.:10.765
## Median :11.752
## Mean :11.958
## 3rd Qu.:12.923
## Max. :21.482SpatialFeaturePlot(
sp_obj,
features = c("mt.content", "rb.content"))
In the case of spatial data, high mitochondrial content is not necessarily an indicator of bad quality spots. Therefore, on its own it is not sufficient to determine which spots to filter out. In this case, they are not pointing towards low quality regions but seem to be reflecting the biological structure of the tissue.
Before filtering:
sp_obj## An object of class Seurat
## 36601 features across 3798 samples within 1 assay
## Active assay: Spatial (36601 features, 0 variable features)We are going to filter out genes that have no expression in the tissue.
table(rowSums(as.matrix(sp_obj@assays$Spatial@counts)) == 0)##
## FALSE TRUE
## 24923 11678keep_genes <- rowSums(as.matrix(sp_obj@assays$Spatial@counts)) != 0
sp_obj <- sp_obj[keep_genes, ]We see how we remove 11.678 genes while keeping 24.923.
Furthermore, we set a threshold to filter out spots with very low number of counts (< 500) before proceeding with the downstream analysis. As we can see there are no spots with <500 UMIs so we will not remove any of them.
table(colSums(as.matrix(sp_obj@assays$Spatial@counts)) < 500)##
## FALSE
## 3798sp_obj <- subset(
sp_obj,
subset = nCount_Spatial > 500)After filtering:
sp_obj## An object of class Seurat
## 24923 features across 3798 samples within 1 assay
## Active assay: Spatial (24923 features, 0 variable features)Similar to single-cell datasets, preprocessing for spatial data requires normalization, identification of variable features and scaling the counts. To carry out these steps we will use SCTransform which takes into account that different spot complexities observed. Spots overlapping more cell-dense regions will have more UMIs. If we use standard Log Normalization we are removing this biological signal from the dataset.
sp_obj <- SCTransform(
sp_obj,
assay = "Spatial", # assay to pull the count data from
ncells = ncol(sp_obj), # Number of subsampling Spots used to build NB regression, in this case use all
variable.features.n = 3000, # variable features to use after ranking by residual variance
verbose = FALSE
)Prior to clustering, we perform dimensionality reduction via PCA. We then look at the elbow plot to assess the right number of principal components (PC) to use for downstream analysis.
sp_obj <- RunPCA(
sp_obj,
npcs = 50
)
ElbowPlot(
sp_obj,
ndims = 50
)
We see an elbow at 15 PC, after that the standard deviations are pretty much flat indicating that they aren’t contributing much information. To reduce computational resources and noise we proceed with the first 15 PCs and add another 10 for a total of 25 PCs to make sure we are not loosing biological signal while reducing the noise.
sp_obj <- RunUMAP(
sp_obj,
reduction = "pca",
dims = 1:25
)Next we compute the K nearest neighbors and find an optimal number of clusters using shared nearest neighbor Louvain modularity based clustering.
sp_obj <- FindNeighbors(
sp_obj,
reduction = "pca",
dims = 1:20
)
sp_obj <- FindClusters(
sp_obj,
resolution = 0.3
)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 3798
## Number of edges: 124079
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9250
## Number of communities: 9
## Elapsed time: 0 secondsLook at the clustering in the UMAP space and on the tissue:
umap_plt <- DimPlot(
sp_obj,
label = TRUE
)
sp_plt <- SpatialDimPlot(
sp_obj
) +
NoLegend()
umap_plt + sp_plt
ESR1 and ERBB2 (HER2) are the two of the most common mutations in breast cancer, so one way of annotating the tissue is by looking at ESR1 and ERBB2 positive/negative regions.
SpatialFeaturePlot(
sp_obj,
features = c("ESR1", "ERBB2"),
alpha = c(0.1, 1)
) +
(SpatialDimPlot(
sp_obj,
label = TRUE
) +
NoLegend())